Description: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.

Description: Using the Internet as a Pricing Tool clinical background information, defining the change in coverage, collecting data, data quality and data limitations. Issue No. 39.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.

Description: Last year, the COVID pandemic significantly increased the pace of change in the life insurance industry, particularly in Accelerated Underwriting (AU). In the past, underwriting process changes occurred relatively infrequently and over a matter of years. Now, with new data sources and predictive models constantly evolving, the frequency of underwriting enhancements and decisioning changes, particularly in AU programs, is occurring every few months. With so much change occurring quickly, how can a company effectively monitor and understand the effects of these changes to make continuous adjustments with confidence and agility? Well-designed data visualizations allow a reviewer to navigate and comprehend detailed results quickly because our brains can process data in a picture format more than 15 times faster than text. Today’s advanced BI tools can make AU program monitoring more efficient and help companies adjust quickly to the results from program changes.